Predicting financial market stress with machine learning | 국외연구자료

KDI 경제교육·정보센터

Economic Information and Education Center

최신자료

Predicting financial market stress with machine learning

BIS

2025.03.24

Using newly constructed market condition indicators (MCIs) for three pivotal US markets (Treasury, foreign exchange, and money markets), we demonstrate that tree-based machine learning (ML) models significantly outperform traditional timeseries approaches in predicting the full distribution of future market stress. Through quantile regression, we show that random forests achieve up to 27% lower quantile loss than autoregressive benchmarks, particularly at longer horizons (3？12 months). Shapley value analysis reveals that funding liquidity, investor overextension and the global financial cycle are important predictors of future tail realizations of market conditions. The MCIs themselves play a prominent role as well, both in the same market (self-reinforcing dynamics within markets) and across markets (spillovers across markets). These results highlight the value of ML in forecasting tail risks and identifying systemic vulnerabilities in real time, bridging the gap between highfrequency data and macroeconomic stability frameworks.

목록보기

1 2 3

KDI 경제교육·정보센터

KDI 경제교육·정보센터

경제정책정보

발행물

경제교육

데이터 분석

멀티콘텐츠

센터소개

연관 사이트

최신자료